Recent N/LAB Projects

Understanding population level money flows around a city has significant potential to drive both policy implications and economic activity. An example of the former is the detection of vulnerable sub-populations while an example of the […]

The collection of geo-demographic data for understanding cities has typically been undertaken by census and other large scale surveys. Providing the basis for a wide range of activity including city planning, market intelligence and policy […]

Land use classification is a critical information to monitor a territory, support its development and plan its evolution. Aerial imagery is generally used as the base media to derive a land use classification by manual […]

Understanding mobility at a population level provides a key role in urban planning. Traditionally undertaken by transport surveys, a key outcome is city wide Origin-Destination (OD) Matrices. While providing known utility, these traditional approaches are […]

Leveraging recent deep learning advances with high resolution drone imagery, the team has developed state-of-the-art techniques for the automatic detection of buildings and roads from aerial imagery. N/LAB is now focusing on the extension of […]

Maps are crucial in emerging countries to make decisions, but often do not exist. Community mapping is a participatory process where community members map their neighbourhoods, making maps openly available in the process. In emerging […]

It has been recently claimed that human movement is ‘highly predictable’ upper bound of 93% predictability shown. However, knowing an upper bound is only useful if it is relatively tight, i.e. it is close to the […]

Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are highly prevalent, particularly in human behavioural data. Continuous, irregular and often highly sparse, these event […]

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Welcome to N/LAB

N/LAB is a centre of excellence at the University of Nottingham in international analytics. We research the use of Big Data and Machine Learning to augment social policy and business decision making. In particular we derive novel forms of demographic intelligence that is generated from digital footprint data streams.

Our research primarily focuses on the development of behavioural segmentation and predictive modelling techniques via novel computational and clustering methods applied to a rich range of data sources (from retail data to communications), provided by their international collaborators across three of the world's fastest growing emerging economies, Tanzania, Malaysia and China.